1. Continuous Deployment of LLM Inference Pods


    Continuous Deployment (CD) of LLM (Large Language Models) Inference Pods can be accomplished using Kubernetes, a powerful platform for automating deployment, scaling, and management of containerized applications.

    In this context, a "Pod" is the smallest deployable unit that can be created and managed by Kubernetes. Each pod encapsulates the application's container (or, in some cases, multiple containers that should run together), storage resources, a unique network IP, and options that govern how the container(s) should run. A Pod represents a unit of deployment, a single instance of an application in Kubernetes.

    To handle the deployment and updating of these pods in a continuous fashion, we will use the Kubernetes Deployment resource. A Deployment provides declarative updates for Pods. You describe the desired state in a Deployment, and the Deployment Controller changes the actual state to the desired state at a controlled rate.

    Here is an example of how you could set up a continuous deployment of an LLM inference service using Pulumi and Kubernetes.

    import pulumi import pulumi_kubernetes as kubernetes # Initialize Kubernetes provider k8s_provider = kubernetes.Provider('k8s') # Define the application deployment app_labels = {'app': 'llm-inference'} deployment = kubernetes.apps.v1.Deployment( 'llm-inference-deployment', spec=kubernetes.apps.v1.DeploymentSpecArgs( replicas=3, # Assumes we want 3 instances for high availability selector=kubernetes.meta.v1.LabelSelectorArgs( match_labels=app_labels ), template=kubernetes.core.v1.PodTemplateSpecArgs( metadata=kubernetes.meta.v1.ObjectMetaArgs( labels=app_labels, ), spec=kubernetes.core.v1.PodSpecArgs( containers=[ kubernetes.core.v1.ContainerArgs( name='llm-inference-container', image='your-registry/llm-inference:latest', # Replace with your actual image ports=[kubernetes.core.v1.ContainerPortArgs(container_port=8080)], # Define resource requirements, environment variables, volumes, and other container resources here ) ], # Include additional configuration such as volumes, security context, etc ) ) ), opts=pulumi.ResourceOptions(provider=k8s_provider) ) # Expose the deployment as a Kubernetes Service service = kubernetes.core.v1.Service( 'llm-inference-service', spec=kubernetes.core.v1.ServiceSpecArgs( selector=app_labels, ports=[kubernetes.core.v1.ServicePortArgs(port=8080)], type='LoadBalancer', ), opts=pulumi.ResourceOptions(provider=k8s_provider) ) # Output the endpoint of the LoadBalancer to access the service pulumi.export('service_endpoint', service.status.apply(lambda s: s.load_balancer.ingress[0].ip if s.load_balancer.ingress else None))

    Explaining the components:

    • Kubernetes Provider: Communicates with the cluster's API server configured during Pulumi setup or from the local kubeconfig file.

    • Deployment: It manages the deployment of LLM inference pods. The replicas parameter defines the number of pod instances. The selector specifies how to find the pods to manage. The template describes the pods launched. It contains metadata like labels and the actual spec defining the container image to use (your-registry/llm-inference:latest should be replaced with the actual image path), the container's exposed ports, and other resources.

    • Service: Exposes the deployment to the internet as a LoadBalancer type service. This automatically provides an external IP to access the inference service.

    After deploying this code with Pulumi, it creates a managed set of pod instances, and the deployment ensures that the specified number of pod copies is always running. If a pod fails, the deployment will automatically recreate it.

    You can update the inference service by changing the container image in the deployment manifest to a new version, and Pulumi will then handle the rollout of the update.

    Lastly, we export the endpoint of the service, which presents the external IP (once provisioned by the cloud provider) where the inference service can be accessed.